On 14 Aug 2001, Nolan Madson wrote:
I have a data set of answers to questions on employee performance.
The answers available are:
Exceeded Expectations
Met Expectations
Did Not Meet Expectations
The answers can be assigned weights [that is, scores -- DFB]
of 3,2,1 (Exceeded, Met,
Donald Burrill wrote:
I agree on all of this. I'd add that at issue is whether people find
the mean format useful, whether it is misleading. I'd use -1, 0 and
+1, rather than 1-3. In this case the mean gives you at-a-glance
summary of the extent to which the people who exceeded expectations
I would like to add that with this kind of data we use the median instead of
the average.
Henry M. Silvert Ph.D.
Research Statistician
The Conference Board
845 3rd. Avenue
New York, NY 10022
Tel. No.: (212) 339-0438
Fax No.: (212) 836-3825
-Original Message-
From: Donald Burrill
Silvert, Henry wrote:
I would like to add that with this kind of data [three-level ordinal]
we use the median instead of the average.
Might I suggest that *neither* is appropriate for most purposes? In
many ways, three-level ordinal data is like dichotomous data - though
there are a
I do not see how (probabilistic) inference is appropriate here at all.
I assume that _all_ employees are rated. There is no sampling, random
or otherwise.
Jon Cryer
At 11:14 AM 8/15/01 -0300, you wrote:
Silvert, Henry wrote:
I would like to add that with this kind of data [three-level
Jon Cryer wrote:
I do not see how (probabilistic) inference is appropriate here at all.
Oh, it never is (strictly), outside of a few industrial
applications. Nobody ever took a random equal-probability sample from
all turnips, all cancer patients, all batches of stainless steel, all
The discussion of categorical data has got me thinking about a
project I am about begin. The goal is to use a variety of individual
predictors (IQ, previous work experience, education, personality) to develop a
model to predict "success" after a vocational rehabilitation program for
If your going to use discriminant analysis you will need a lot of data and
it does assume the predictors are multivariate normal. Generalized linear
models would seem best, particularly in the event that you don't know if
they are ordinal. You can do a multinomial followed by a cummulative logit
Nolan Madson writes:
I have a data set of answers to questions on employee performance.
The answers available are:
Exceeded Expectations
Met Expectations
Did Not Meet Expectations
The answers can be assigned weights of 3,2,1 (Exceeded, Met, Did Not
Meet).
One of my colleagues says that it is
On 15 Aug 2001 09:57:27 -0700, [EMAIL PROTECTED] (Paul R.
Swank) wrote:
PRS If your going to use discriminant analysis you will need a lot
of data and it does assume the predictors are multivariate normal.
- well, logistic has to assume (almost) the same thing, almost as
strongly, when it has
Cristian,
Let me set you straight.
Whomever did you the disservice of teaching you the DW should be
scolded.
DW only measures 1 period lag.
Box-Jenkins methodology uses the Autocorrelation function and partial
correlation function to evaluate all lags.
I suggest that you look for the
Date: Tue, 14 AUG 2001 16:27:11 +1000
From: Hong Ooi [EMAIL PROTECTED]
On 13 Aug 2001 18:59:10 -0700, [EMAIL PROTECTED] (David
Goldsmith) wrote:
Aloha! I'm fitting theoretically normally distributed data, of widely
differing sample sizes, to Gaussians by histograming it and then using an
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